Advanced Data Science

Advanced Data Science

  • Training your TensorFlow models in the cloud52:21

    Training your TensorFlow models in the cloud

    TensorFlow is an open-source software library for numerical computation using data flow graphs.

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  • A new set of IDE features to help you and your team work better and faster together46:23

    A new set of IDE features to help you and your team work better and faster together

    RStudio Server Pro has a whole new set of features designed to help you and your team work better and faster together.

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  • Thinking inside the box: you can do that inside a data frame?!43:40

    Thinking inside the box: you can do that inside a data frame?!

    The data frame is a crucial data structure in R and, especially, in the tidyverse. Working on a column or a variable is a very natural operation, which is great. But what about row-oriented work?

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  • Creating and Preprocessing a Design Matrix with Recipes55:42

    Creating and Preprocessing a Design Matrix with Recipes

    In this talk, a new package called recipes is shown where the specification of model terms and preprocessing steps can be enumerated sequentially.

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  • Collaboration and time travel- version control with git, github and RStudio48:56

    Collaboration and time travel- version control with git, github and RStudio

    Hadley Wickham presents and demonstrates how understanding git & github will give you two data science superpowers.

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  • Use JavaScript visualization libraries at the R console56:26

    Use JavaScript visualization libraries at the R console

    HTMLwidgets are always hosted within an R package and include all of the source code for their dependencies. This is to ensure that code which depends on widgets doesn’t require an internet connection

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  • Effective frameworks for thinking about data analysis/data science problems in R47:16

    Effective frameworks for thinking about data analysis/data science problems in R

    Hadley Wickham Chief Scientist at RStudio and Adjunct Professor of Statistics at Rice University will discuss broadly an effective framework for thinking about data analysis/data science problems in R

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